Training and using artificial intelligence (ai) / machine learning (ml) models to automatically supplement and/or complete code of robotic process automation workflows

ABSTRACT

Training and using artificial intelligence (AI)/machine learning (ML) models to automatically supplement and/or complete code of RPA workflows is disclosed. A trained AI/ML model may intelligently and automatically predict and complete the next series of activities in RPA workflows (e.g., one, a few, many, the remainder of the workflow, etc.). Actions users take while creating workflows over a time period may be captured and stored. The AI/ML model may then be trained and used to match the stored actions with stored workflow sequences of actions in order to predict and complete the workflow. As more and more workflow sequences are captured and stored over time, the AI/ML model may be retrained to predict a larger number of sequences and/or to more accurately make predictions. Auto-completion may occur in real-time in some embodiments to save time and effort by the user.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part of, and claims the benefitof, U.S. Nonprovisional patent application Ser. No. 16/702,966 filedDec. 4, 2019, which claims the benefit of Indian Patent Application No.201911041766 filed Oct. 15, 2019. The subject matter of these earlierfiled applications is hereby incorporated by reference in its entirety.

FIELD

The present invention generally relates to robotic process automation(RPA), and more specifically, to training and using artificialintelligence (AI)/machine learning (ML) models to automaticallysupplement and/or complete code of RPA workflows.

BACKGROUND

An RPA workflow may include many modules and/or sequences. When creatingworkflows for similar tasks, users often tend to repeat certain steps orsequences. Repeating these steps in the workflow takes more developertime and reduces productivity. Existing solutions providetemplate-driven generation of workflow designs for business automation.However, these templates are preset and do not include intelligence inpredicting the user intent or requirements, let alone take into accountchanges therein. Accordingly, an improved solution that reduces oravoids such repetition during workflow creation may be beneficial.

SUMMARY

Certain embodiments of the present invention may provide solutions tothe problems and needs in the art that have not yet been fullyidentified, appreciated, or solved by current RPA technologies. Forexample, some embodiments of the present invention pertain to trainingand using AI/ML models to automatically supplement and/or complete codeof RPA workflows.

In an embodiment, a system includes a developer computing systemexecuting an RPA designer application and a model serving server hostingone or more AI/ML models trained to analyze sequences of activities inan RPA workflow as input and provide suggestions of next sequences ofactivities and respective confidence scores as an output. The RPAdesigner application is configured to capture a sequence of theactivities in an RPA workflow, send the captured sequence of activitiesto the model serving server, receive one or more suggested nextsequences of activities from the one or more trained AI/ML models viathe model serving server, and display the one or more suggested nextsequences of activities to the developer.

In another embodiment, a non-transitory computer-readable medium storesa computer program including an RPA designer application. The computerprogram is configured to cause at least one processor to capture asequence of the activities in an RPA workflow. The captured sequence ofactivities includes one or more activities that have been added toand/or modified in the RPA workflow by a developer. The computer programis also configured to cause the at least one processor to send thecaptured sequence of activities to a model serving server, receive oneor more suggested next sequences of activities from one or more trainedAI/ML models via the model serving server, and display the one or moresuggested next sequences of activities to the developer.

In yet another embodiment, a model serving computing system includesmemory storing computer program instructions and at least one processorconfigured to execute the computer program instructions. The computerprogram instructions are configured to cause the at least one processorto receive a captured sequence of activities in an RPA workflow underdevelopment from an RPA designer application of a developer computingsystem via a communication network, provide the captured sequence ofactivities as input to one or more trained AI/ML models, receive one ormore suggested next sequences of activities and respective confidencescores as an output from the one or more trained AI/ML models, and sendthe one or more suggested next sequences of activities to the designercomputing system.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of certain embodiments of the inventionwill be readily understood, a more particular description of theinvention briefly described above will be rendered by reference tospecific embodiments that are illustrated in the appended drawings.While it should be understood that these drawings depict only typicalembodiments of the invention and are not therefore to be considered tobe limiting of its scope, the invention will be described and explainedwith additional specificity and detail through the use of theaccompanying drawings, in which:

FIG. 1 is an architectural diagram illustrating an RPA system, accordingto an embodiment of the present invention.

FIG. 2 is an architectural diagram illustrating a deployed RPA system,according to an embodiment of the present invention.

FIG. 3 is an architectural diagram illustrating the relationship betweena designer, activities, and drivers, according to an embodiment of thepresent invention.

FIG. 4 is an architectural diagram illustrating an RPA system, accordingto an embodiment of the present invention.

FIG. 5 is an architectural diagram illustrating a computing systemconfigured to train and/or use AI/ML models to automatically supplementand/or complete code of RPA workflows, according to an embodiment of thepresent invention.

FIG. 6A illustrates an example of a neural network that has been trainedto recognize graphical elements in an image, according to an embodimentof the present invention.

FIG. 6B illustrates an example of a neuron, according to an embodimentof the present invention.

FIG. 7 is a flowchart illustrating a process for training AI/ML model(s)to provide suggestions to automatically add to and/or complete RPAworkflows, according to an embodiment of the present invention.

FIG. 8A is a screenshot illustrating an RPA designer application after apotential next sequence of activities has been detected by an ML model,according to an embodiment of the present invention.

FIG. 8B is a screenshot illustrating the RPA designer application ofFIG. 8A after the user has indicated that the suggested next sequence ofactivities is correct and the sequence has been added to the workflow,according to an embodiment of the present invention.

FIG. 8C is a screenshot illustrating an auto-completed variables tab,according to an embodiment of the present invention.

FIG. 8D is a screenshot illustrating an auto-completed properties tab,according to an embodiment of the present invention.

FIG. 9 is a flow diagram illustrating a process for rejecting oraccepting and automatically completing a suggested next sequence ofactivities for an RPA workflow, according to an embodiment of thepresent invention.

FIG. 10 is an autocompletion architectural diagram for both apersonalized and generalized flow, according to an embodiment of thepresent invention.

FIG. 11 is architectural diagram illustrating a system configured totrain and deploy AI/ML models that provide suggestions to automaticallyadd to or complete RPA workflows, according to an embodiment of thepresent invention.

FIG. 12 illustrates an example RPA workflow, according to an embodimentof the present invention.

FIGS. 13A and 13B illustrate a first part and a second part,respectively, of the XAML in an RPA designer application that has beenprepared based on activities added and configured by an RPA developer upto a certain point in developing the RPA workflow of FIG. 12, accordingto an embodiment of the present invention.

FIGS. 14A and 14B illustrate a first part and a second part,respectively, of the XAML for the RPA workflow of FIG. 12 with XAML fora predicted next suitable activity highlighted in grey, according to anembodiment of the present invention.

FIG. 15 is a flowchart illustrating a process for using AI/ML models toautomatically supplement and/or complete RPA workflows, according to anembodiment of the present invention.

Unless otherwise indicated, similar reference characters denotecorresponding features consistently throughout the attached drawings.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Some embodiments pertain to training and using AI/ML models toautomatically supplement and/or complete code of RPA workflows. As usedherein, ML may refer to deep learning (DL) (e.g., deep learning neuralnetworks (DLNNs)), shallow learning (e.g., shallow learning neuralnetworks (SLNNs)), any other suitable type of machine learning, sentencetransformers, or any combination thereof without deviating from thescope of the invention. Such embodiments may intelligently andautomatically predict and complete the next series of activities in RPAworkflows (e.g., one, a few, many, the remainder of the workflow, etc.)using ML techniques, or potentially complete the RPA workflow entirely.Activities that users create and/or modify while creating RPA workflowsmay be captured and stored in a database over a period of time in asuitable format (e.g., extensible application markup language (XAML),JavaScript Object Notation (JSON), XML, plain text, fields in adatabase, etc.). An ML model (also referred to as an AI/ML model herein)may then be trained on a suitable dataset (e.g., an XAML and/or JSONfile dataset) that includes the RPA workflows containing sequences ofactivities created by RPA developers. Such files may contain theinformation used to create RPA workflows (e.g., activities, parameters,activity flow, etc.). Example XAML and JSON files for a basic RPAworkflow are provided below.

Once the ML model is trained, files (e.g., XAML files) storinginformation with respect to a workflow that a developer is currentlybuilding may be passed by an RPA designer application as input data tothe ML model. The ML model may then consume this input and predict oneor more potential next sequences of activities for autocompletion, alongwith a confidence score. If the confidence score(s) exceed a suggestionthreshold (e.g., 75%, 90%, etc.), the next sequence(s) may be displayedto the user or completed automatically by the RPA designer application.Autocompletion may have its own, higher threshold in some embodiments(e.g., 95%, 99%, etc.). The format of this predicted output may also bean XAML file in some embodiments. If the current sequence of activitiesin the workflow does not lead to a prediction of a next sequence ofactivities with at least a predetermined suggestion confidence threshold(i.e., a suggestion confidence threshold), the RPA designer applicationmay continue to periodically pass the workflow information to the XAMLmodel as the developer continues to create the workflow, and at thatpoint, a suggestion of a next sequence of activities may be provided ifthe prediction meets the suggestion confidence threshold. Thus, thetraining data may include incomplete RPA workflows in addition tocompleted RPA workflows in some embodiments.

Training of a global ML model may occur on the server side so a largercache of workflow data from multiple or many RPA developers can bestored and analyzed to find patterns. Also, servers tend to have heavyprocessing and graphical processing unit (GPU) resources, which allowstraining to occur more rapidly using this machinery. However, trainingof one or more ML models may occur on the same computing system, ondifferent computing systems, on the client side, on the server side, oron any other computing system(s) and/or location(s) without deviatingfrom the scope of the invention.

In some embodiments, local models may be trained for each RPA developerto take into account individual developer styles and preferences. Forexample, a developer may prefer to send a certain email after a sequenceof activities, may prefer certain variable types, etc. It may bedesirable to train local ML models for specific users on their owncomputing systems, if possible given the hardware resources of thecomputing system, since that local ML model with user-specificpreferences may only be used for that user in some embodiments. Oncetrained, global and local ML models could be pushed to the RPA developerapplication or made available to the RPA application remotely (e.g.,executed on the server side at the request of the RPA developerapplication). If no local ML model has been developed for that user, theglobal ML model can be used. In some embodiments, the local ML model maybe applied first, and if no next sequence is predicted (e.g., theconfidence threshold for the local model is not met), the global MLmodel may then be applied to attempt to find a sequence for suggestion.In certain embodiments, the local ML model and the global ML model mayhave different suggestion confidence thresholds. For instance, the localML model may be more accurate for a given user than the global ML model,and thus have a lower suggestion threshold, or vice versa.

Because it learns from workflow data from multiple or many RPAdevelopers, the global ML model may be updated less frequently and maytake longer to train. The local ML model, on the other hand, uses theworkflow data from a given developer. Thus, in some embodiments, theglobal model may be updated every few weeks, whereas the local model maybe updated every few days. Naturally, the speed and frequency with whicheach model can be trained depends on the given implementation andprocessing resources.

Once trained, the ML model(s) may receive user confirmation with respectto the auto-created sequences of the RPA workflow in order to completethat portion of the RPA workflow. As more and more RPA workflowsequences are captured and stored over time, the ML model(s) may beretrained to predict a larger number of sequences and/or to moreaccurately make predictions. Auto-completion may occur in real-time insome embodiments to save time and effort by the developer or other user.

In some embodiments, a developer may start building his or her RPAworkflow from scratch. As a step (i.e., an activity) is added to theworkflow, the ML model(s) (local, global, or both) may analyze the step,and potentially one or more preceding steps in a sequence, and checkwhether one or more sequences may potentially be desired following thatstep that meet at least a predetermined probabilistic suggestionthreshold. Once the user adds an activity to the workflow, the last Nactivities including this newly added activity, or potentially allprevious activities, may be considered by the ML model to check whethera next logical sequence of activities can be predicted andautocompleted. This possibility may be determined by the confidencescore of the ML model prediction, which may be above 90% in someembodiments. If the confidence score for stored sequences to besuggested based on the current sequence of activities in the workflow isless than the suggestion confidence threshold, then no suggestion may beprovided. The ML model may then be run again when the next activity isadded until the suggestion confidence threshold is met. Thus, theconfidence score(s) for predicted next sequence(s) and the suggestionconfidence threshold may be used to determine whether to suggest a givensequence from the ML model.

It is possible that more than one potential next sequences of activitiesmay exceed the suggestion confidence threshold. If this is the case, theuser/developer may be presented with these sequences as options topotentially complete one or more next steps in the RPA workflow. Incertain embodiments, the sequences are ranked in order of theirrespective confidence scores. The user/developer may then select thepertinent next sequence, which is automatically added to the workflow.The user may also decline to add any suggested next sequences.

In certain embodiments, adding the selected next sequence of activitiesmay include adding the activities to the RPA workflow, settingdeclarations and usage of variables (i.e., programming variables),reading from/writing to certain files, and/or any other desiredpertinent steps to logically conclude a sequence in an RPA workflowwithout deviating from the scope of the invention. An RPA workflow,somewhat similar to a programming language, typically has variables ofdifferent types that are used during execution of the RPA workflow. Ifthese variables are not declared as a proper datatype, the RPA workflowmay run into errors. Thus, correct data types of a variables to holdnumbers (e.g., Integer), text (e.g., String), etc. should be selected.Thus, some embodiments both perform autocompletion of RPA workflows andinternally declare the associated variables of the correct typeintelligently.

Per the above, in some embodiments, the ML model provides sequencepredictions that meet or exceed a suggestion confidence threshold (i.e.,the estimated probability by the ML model that a subsequence will beused following a given step or activity, or sequence thereof). The MLmodel may learn the confidence score based on training using manyworkflows as a whole and sequences within these workflows. Also, per theabove, if multiple sequences exceed the suggestion confidence thresholdfor a given step (e.g., at least two logical branches exist since two ormore sequences have a confidence score that meets or exceeds thethreshold) the developer may be prompted with these sequences. Thedeveloper may then choose which sequence is correct (or in someembodiments, indicate that no sequence is correct). If a sequence isselected, the selected sequence is automatically added into theworkflow. If not, the developer continues the workflow developmentprocess. In some embodiments, over a period of time, the ML model maylearn more and more about the developer's personal style, logic, andconventions. The ML model may then use this information to predict andcomplete the workflow based on how the ML model estimates that thedeveloper would have personally desired. For instance, one developer mayprefer to bring screens to the front when entering data to fieldstherein so that users can see the RPA robot in action whereas anotherdeveloper may not care whether the screen is visible and may prefer toomit such a step in favor of automation speed, despite both preferencesachieving the same result.

In some embodiments, the ML model may be trained via attended feedback,unattended feedback, or both. Attended feedback includes where thedeveloper is actively involved in producing the training data. Forinstance, the RPA developer may be prompted for reasons why he or shedid not want to use the predicted next sequence of activities andprovide this to the server side for training. Unattended feedbackincludes information gleaned without the user's active participation (orpotentially, knowledge). For instance, the mere fact that a user hasrejected the sequence of activities may provide information that the MLmodel may not be working as intended for that given user. The activitiesthat the developer includes in the workflow after rejecting thesuggestion may then be used to train the model regarding what thedeveloper is actually looking for. If this tends to be the caseglobally, this information could also be used to train the global MLmodel.

The attended feedback, unattended feedback, or both, provide input fortraining the local and global ML models. The global ML model is ageneralized model for all RPA developers or a subset of RPA developers,and the local ML model is personalized and user-specific. If the localML model does not exist or does not find a sequence that meets orexceeds the suggestion confidence threshold, the global ML model may beconsulted to attempt to find a suggestion meets or exceeds thesuggestion confidence threshold for prediction. In certain embodiments,more than two ML models may be used. For example, some embodiments mayemploy a local model for a given developer and then N next models (e.g.,programming team, then group, then company, etc.) that apply toincreasingly large groups of developers, all the way up to a globalmodel. These ML models may be applied in a sequence in some embodiments(e.g., the ML model that applies to the smallest number of users to thatwhich applies to the largest number).

FIG. 1 is an architectural diagram illustrating an RPA system 100,according to an embodiment of the present invention. RPA system 100includes a designer 110 that allows a developer to design and implementworkflows. Designer 110 may provide a solution for applicationintegration, as well as automating third-party applications,administrative Information Technology (IT) tasks, and business ITprocesses. Designer 110 may facilitate development of an automationproject, which is a graphical representation of a business process.Simply put, designer 110 facilitates the development and deployment ofworkflows and robots.

The automation project enables automation of rule-based processes bygiving the developer control of the execution order and the relationshipbetween a custom set of steps developed in a workflow, defined herein as“activities.” One commercial example of an embodiment of designer 110 isUiPath Studio™. Each activity may include an action, such as clicking abutton, reading a file, writing to a log panel, etc. In someembodiments, workflows may be nested or embedded.

Some types of workflows may include, but are not limited to, sequences,flowcharts, Finite State Machines (FSMs), and/or global exceptionhandlers. Sequences may be particularly suitable for linear processes,enabling flow from one activity to another without cluttering aworkflow. Flowcharts may be particularly suitable to more complexbusiness logic, enabling integration of decisions and connection ofactivities in a more diverse manner through multiple branching logicoperators. FSMs may be particularly suitable for large workflows. FSMsmay use a finite number of states in their execution, which aretriggered by a condition (i.e., transition) or an activity. Globalexception handlers may be particularly suitable for determining workflowbehavior when encountering an execution error and for debuggingprocesses.

Once a workflow is developed in designer 110, execution of businessprocesses is orchestrated by conductor 120, which orchestrates one ormore robots 130 that execute the workflows developed in designer 110.One commercial example of an embodiment of conductor 120 is UiPathOrchestrator™. Conductor 120 facilitates management of the creation,monitoring, and deployment of resources in an environment. Conductor 120may act as an integration point with third-party solutions andapplications.

Conductor 120 may manage a fleet of robots 130, connecting and executingrobots 130 from a centralized point. Types of robots 130 that may bemanaged include, but are not limited to, attended robots 132, unattendedrobots 134, development robots (similar to unattended robots 134, butused for development and testing purposes), and nonproduction robots(similar to attended robots 132, but used for development and testingpurposes). Attended robots 132 are triggered by user events and operatealongside a human on the same computing system. Attended robots 132 maybe used with conductor 120 for a centralized process deployment andlogging medium. Attended robots 132 may help the human user accomplishvarious tasks, and may be triggered by user events. In some embodiments,processes cannot be started from conductor 120 on this type of robotand/or they cannot run under a locked screen. In certain embodiments,attended robots 132 can only be started from a robot tray or from acommand prompt. Attended robots 132 should run under human supervisionin some embodiments.

Unattended robots 134 run unattended in virtual environments and canautomate many processes. Unattended robots 134 may be responsible forremote execution, monitoring, scheduling, and providing support for workqueues. Debugging for all robot types may be run in designer 110 in someembodiments. Both attended and unattended robots may automate varioussystems and applications including, but not limited to, mainframes, webapplications, VMs, enterprise applications (e.g., those produced bySAP®, SalesForce®, Oracle®, etc.), and computing system applications(e.g., desktop and laptop applications, mobile device applications,wearable computer applications, etc.).

Conductor 120 may have various capabilities including, but not limitedto, provisioning, deployment, configuration, queueing, monitoring,logging, and/or providing interconnectivity. Provisioning may includecreating and maintenance of connections between robots 130 and conductor120 (e.g., a web application). Deployment may include assuring thecorrect delivery of package versions to assigned robots 130 forexecution. Configuration may include maintenance and delivery of robotenvironments and process configurations. Queueing may include providingmanagement of queues and queue items. Monitoring may include keepingtrack of robot identification data and maintaining user permissions.Logging may include storing and indexing logs to a database (e.g., anSQL database) and/or another storage mechanism (e.g., ElasticSearch®,which provides the ability to store and quickly query large datasets).Conductor 120 may provide interconnectivity by acting as the centralizedpoint of communication for third-party solutions and/or applications.

Robots 130 are execution agents that run workflows built in designer110. One commercial example of some embodiments of robot(s) 130 isUiPath Robots™. In some embodiments, robots 130 install the MicrosoftWindows® Service Control Manager (SCM)-managed service by default. As aresult, such robots 130 can open interactive Windows® sessions under thelocal system account, and have the rights of a Windows® service.

In some embodiments, robots 130 can be installed in a user mode. Forsuch robots 130, this means they have the same rights as the user underwhich a given robot 130 has been installed. This feature may also beavailable for High Density (HD) robots, which ensure full utilization ofeach machine at its maximum potential. In some embodiments, any type ofrobot 130 may be configured in an HD environment.

Robots 130 in some embodiments are split into several components, eachbeing dedicated to a particular automation task. The robot components insome embodiments include, but are not limited to, SCM-managed robotservices, user mode robot services, executors, agents, and command line.SCM-managed robot services manage and monitor Windows® sessions and actas a proxy between conductor 120 and the execution hosts (i.e., thecomputing systems on which robots 130 are executed). These services aretrusted with and manage the credentials for robots 130. A consoleapplication is launched by the SCM under the local system.

User mode robot services in some embodiments manage and monitor Windows®sessions and act as a proxy between conductor 120 and the executionhosts. User mode robot services may be trusted with and manage thecredentials for robots 130. A Windows® application may automatically belaunched if the SCM-managed robot service is not installed.

Executors may run given jobs under a Windows® session (i.e., they mayexecute workflows. Executors may be aware of per-monitor dots per inch(DPI) settings. Agents may be Windows® Presentation Foundation (WPF)applications that display the available jobs in the system tray window.Agents may be a client of the service. Agents may request to start orstop jobs and change settings. The command line is a client of theservice. The command line is a console application that can request tostart jobs and waits for their output.

Having components of robots 130 split as explained above helpsdevelopers, support users, and computing systems more easily run,identify, and track what each component is executing. Special behaviorsmay be configured per component this way, such as setting up differentfirewall rules for the executor and the service. The executor may alwaysbe aware of DPI settings per monitor in some embodiments. As a result,workflows may be executed at any DPI, regardless of the configuration ofthe computing system on which they were created. Projects from designer110 may also be independent of browser zoom level in some embodiments.For applications that are DPI-unaware or intentionally marked asunaware, DPI may be disabled in some embodiments.

FIG. 2 is an architectural diagram illustrating a deployed RPA system200, according to an embodiment of the present invention. In someembodiments, RPA system 200 may be, or may be a part of, RPA system 100of FIG. 1. It should be noted that the client side, the server side, orboth, may include any desired number of computing systems withoutdeviating from the scope of the invention. On the client side, a robotapplication 210 includes executors 212, an agent 214, and a designer216. However, in some embodiments, designer 216 may not be running oncomputing system 210. Executors 212 are running processes. Severalbusiness projects may run simultaneously, as shown in FIG. 2. Agent 214(e.g., a Windows® service) is the single point of contact for allexecutors 212 in this embodiment. All messages in this embodiment arelogged into conductor 230, which processes them further via databaseserver 240, indexer server 250, or both. As discussed above with respectto FIG. 1, executors 212 may be robot components.

In some embodiments, a robot represents an association between a machinename and a username. The robot may manage multiple executors at the sametime. On computing systems that support multiple interactive sessionsrunning simultaneously (e.g., Windows® Server 2012), multiple robots maybe running at the same time, each in a separate Windows® session using aunique username. This is referred to as HD robots above.

Agent 214 is also responsible for sending the status of the robot (e.g.,periodically sending a “heartbeat” message indicating that the robot isstill functioning) and downloading the required version of the packageto be executed. The communication between agent 214 and conductor 230 isalways initiated by agent 214 in some embodiments. In the notificationscenario, agent 214 may open a WebSocket channel that is later used byconductor 230 to send commands to the robot (e.g., start, stop, etc.).

On the server side, a presentation layer (web application 232, Open DataProtocol (OData) Representative State Transfer (REST) ApplicationProgramming Interface (API) endpoints 234, and notification andmonitoring 236), a service layer (API implementation/business logic238), and a persistence layer (database server 240 and indexer server250) are included. Conductor 230 includes web application 232, ODataREST API endpoints 234, notification and monitoring 236, and APIimplementation/business logic 238. In some embodiments, most actionsthat a user performs in the interface of conductor 230 (e.g., viabrowser 220) are performed by calling various APIs. Such actions mayinclude, but are not limited to, starting jobs on robots,adding/removing data in queues, scheduling jobs to run unattended, etc.without deviating from the scope of the invention. Web application 232is the visual layer of the server platform. In this embodiment, webapplication 232 uses Hypertext Markup Language (HTML) and JavaScript(JS). However, any desired markup languages, script languages, or anyother formats may be used without deviating from the scope of theinvention. The user interacts with web pages from web application 232via browser 220 in this embodiment in order to perform various actionsto control conductor 230. For instance, the user may create robotgroups, assign packages to the robots, analyze logs per robot and/or perprocess, start and stop robots, etc.

In addition to web application 232, conductor 230 also includes servicelayer that exposes OData REST API endpoints 234. However, otherendpoints may be included without deviating from the scope of theinvention. The REST API is consumed by both web application 232 andagent 214. Agent 214 is the supervisor of one or more robots on theclient computer in this embodiment.

The REST API in this embodiment covers configuration, logging,monitoring, and queueing functionality. The configuration endpoints maybe used to define and configure application users, permissions, robots,assets, releases, and environments in some embodiments. Logging RESTendpoints may be used to log different information, such as errors,explicit messages sent by the robots, and other environment-specificinformation, for instance. Deployment REST endpoints may be used by therobots to query the package version that should be executed if the startjob command is used in conductor 230. Queueing REST endpoints may beresponsible for queues and queue item management, such as adding data toa queue, obtaining a transaction from the queue, setting the status of atransaction, etc.

Monitoring REST endpoints may monitor web application 232 and agent 214.Notification and monitoring API 236 may be REST endpoints that are usedfor registering agent 214, delivering configuration settings to agent214, and for sending/receiving notifications from the server and agent214. Notification and monitoring API 236 may also use WebSocketcommunication in some embodiments.

The persistence layer includes a pair of servers in thisembodiment—database server 240 (e.g., a SQL server) and indexer server250. Database server 240 in this embodiment stores the configurations ofthe robots, robot groups, associated processes, users, roles, schedules,etc. This information is managed through web application 232 in someembodiments. Database server 240 may manages queues and queue items. Insome embodiments, database server 240 may store messages logged by therobots (in addition to or in lieu of indexer server 250).

Indexer server 250, which is optional in some embodiments, stores andindexes the information logged by the robots. In certain embodiments,indexer server 250 may be disabled through configuration settings. Insome embodiments, indexer server 250 uses ElasticSearch®, which is anopen source project full-text search engine. Messages logged by robots(e.g., using activities like log message or write line) may be sentthrough the logging REST endpoint(s) to indexer server 250, where theyare indexed for future utilization.

FIG. 3 is an architectural diagram illustrating the relationship 300between a designer 310, activities 320, 330, drivers 340, and AI/MLmodels 350, according to an embodiment of the present invention. Per theabove, a developer uses designer 310 to develop workflows that areexecuted by robots. Workflows may include user-defined activities 320and UI automation activities 330. User-defined activities 320 and/or UIautomation activities 330 may call one or more AI/ML models 350 in someembodiments, which may be located locally to the computing system onwhich the robot is operating and/or remotely thereto. Some embodimentsare able to identify non-textual visual components in an image, which iscalled computer vision (CV) herein. Some CV activities pertaining tosuch components may include, but are not limited to, click, type, gettext, hover, element exists, refresh scope, highlight, etc. Click insome embodiments identifies an element using CV, optical characterrecognition (OCR), fuzzy text matching, and multi-anchor, for example,and clicks it. Type may identify an element using the above and types inthe element. Get text may identify the location of specific text andscan it using OCR. Hover may identify an element and hover over it.Element exists may check whether an element exists on the screen usingthe techniques described above. In some embodiments, there may behundreds or even thousands of activities that can be implemented indesigner 310. However, any number and/or type of activities may beavailable without deviating from the scope of the invention.

UI automation activities 330 are a subset of special, lower levelactivities that are written in lower level code (e.g., CV activities)and facilitate interactions with the screen. UI automation activities330 facilitate these interactions via drivers 340 and/or AI/ML models350 that allow the robot to interact with the desired software. One ormore of AI/ML models 350 may be used by UI automation activities 330 inorder to perform interactions with the computing system. For instance,drivers 340 may include OS drivers 342, browser drivers 344, VM drivers346, enterprise application drivers 348, etc.

Drivers 340 may interact with the OS at a low level looking for hooks,monitoring for keys, etc. They may facilitate integration with Chrome®,IE®, Citrix®, SAP®, etc. For instance, the “click” activity performs thesame role in these different applications via drivers 340.

FIG. 4 is an architectural diagram illustrating an RPA system 400,according to an embodiment of the present invention. In someembodiments, RPA system 400 may be or include RPA systems 100 and/or 200of FIGS. 1 and/or 2. RPA system 400 includes multiple client computingsystems 410 running robots. Computing systems 410 are able tocommunicate with a conductor computing system 420 via a web applicationrunning thereon. Conductor computing system 420, in turn, is able tocommunicate with a database server 430 and an optional indexer server440.

With respect to FIGS. 1 and 3, it should be noted that while a webapplication is used in these embodiments, any suitable client/serversoftware may be used without deviating from the scope of the invention.For instance, the conductor may run a server-side application thatcommunicates with non-web-based client software applications on theclient computing systems.

FIG. 5 is an architectural diagram illustrating a computing system 500configured to train and/or use AI/ML models to automatically supplementand/or complete code of RPA workflows, according to an embodiment of thepresent invention. In some embodiments, computing system 500 may be oneor more of the computing systems depicted and/or described herein.Computing system 500 includes a bus 505 or other communication mechanismfor communicating information, and processor(s) 510 coupled to bus 505for processing information. Processor(s) 510 may be any type of generalor specific purpose processor, including a Central Processing Unit(CPU), an Application Specific Integrated Circuit (ASIC), a FieldProgrammable Gate Array (FPGA), a Graphics Processing Unit (GPU),multiple instances thereof, and/or any combination thereof. Processor(s)510 may also have multiple processing cores, and at least some of thecores may be configured to perform specific functions. Multi-parallelprocessing may be used in some embodiments. In certain embodiments, atleast one of processor(s) 510 may be a neuromorphic circuit thatincludes processing elements that mimic biological neurons. In someembodiments, neuromorphic circuits may not require the typicalcomponents of a Von Neumann computing architecture.

Computing system 500 further includes a memory 515 for storinginformation and instructions to be executed by processor(s) 510. Memory515 can be comprised of any combination of Random Access Memory (RAM),Read Only Memory (ROM), flash memory, cache, static storage such as amagnetic or optical disk, or any other types of non-transitorycomputer-readable media or combinations thereof. Non-transitorycomputer-readable media may be any available media that can be accessedby processor(s) 510 and may include volatile media, non-volatile media,or both. The media may also be removable, non-removable, or both.

Additionally, computing system 500 includes a communication device 520,such as a transceiver, to provide access to a communications network viaa wireless and/or wired connection. In some embodiments, communicationdevice 520 may be configured to use Frequency Division Multiple Access(FDMA), Single Carrier FDMA (SC-FDMA), Time Division Multiple Access(TDMA), Code Division Multiple Access (CDMA), Orthogonal FrequencyDivision Multiplexing (OFDM), Orthogonal Frequency Division MultipleAccess (OFDMA), Global System for Mobile (GSM) communications, GeneralPacket Radio Service (GPRS), Universal Mobile Telecommunications System(UMTS), cdma2000, Wideband CDMA (W-CDMA), High-Speed Downlink PacketAccess (HSDPA), High-Speed Uplink Packet Access (HSUPA), High-SpeedPacket Access (HSPA), Long Term Evolution (LTE), LTE Advanced (LTE-A),802.11x, Wi-Fi, Zigbee, Ultra-WideBand (UWB), 802.16x, 802.15, HomeNode-B (HnB), Bluetooth, Radio Frequency Identification (RFID), InfraredData Association (IrDA), Near-Field Communications (NFC), fifthgeneration (5G), New Radio (NR), any combination thereof, and/or anyother currently existing or future-implemented communications standardand/or protocol without deviating from the scope of the invention. Insome embodiments, communication device 520 may include one or moreantennas that are singular, arrayed, phased, switched, beamforming,beamsteering, a combination thereof, and or any other antennaconfiguration without deviating from the scope of the invention.

Processor(s) 510 are further coupled via bus 505 to a display 525, suchas a plasma display, a Liquid Crystal Display (LCD), a Light EmittingDiode (LED) display, a Field Emission Display (FED), an Organic LightEmitting Diode (OLED) display, a flexible OLED display, a flexiblesubstrate display, a projection display, a 4K display, a high definitiondisplay, a Retina® display, an In-Plane Switching (IPS) display, or anyother suitable display for displaying information to a user. Display 525may be configured as a touch (haptic) display, a three dimensional (3D)touch display, a multi-input touch display, a multi-touch display, etc.using resistive, capacitive, surface-acoustic wave (SAW) capacitive,infrared, optical imaging, dispersive signal technology, acoustic pulserecognition, frustrated total internal reflection, etc. Any suitabledisplay device and haptic I/O may be used without deviating from thescope of the invention.

A keyboard 530 and a cursor control device 535, such as a computermouse, a touchpad, etc., are further coupled to bus 505 to enable a userto interface with computing system. However, in certain embodiments, aphysical keyboard and mouse may not be present, and the user mayinteract with the device solely through display 525 and/or a touchpad(not shown). Any type and combination of input devices may be used as amatter of design choice. In certain embodiments, no physical inputdevice and/or display is present. For instance, the user may interactwith computing system 500 remotely via another computing system incommunication therewith, or computing system 500 may operateautonomously.

Memory 515 stores software modules that provide functionality whenexecuted by processor(s) 510. The modules include an operating system540 for computing system 500. The modules further include an automaticworkflow completion module 545 that is configured to perform all or partof the processes described herein or derivatives thereof. Computingsystem 500 may include one or more additional functional modules 550that include additional functionality.

One skilled in the art will appreciate that a “system” could be embodiedas a server, an embedded computing system, a personal computer, aconsole, a personal digital assistant (PDA), a cell phone, a tabletcomputing device, a quantum computing system, or any other suitablecomputing device, or combination of devices without deviating from thescope of the invention. Presenting the above-described functions asbeing performed by a “system” is not intended to limit the scope of thepresent invention in any way, but is intended to provide one example ofthe many embodiments of the present invention. Indeed, methods, systems,and apparatuses disclosed herein may be implemented in localized anddistributed forms consistent with computing technology, including cloudcomputing systems. The computing system could be part of or otherwiseaccessible by a local area network (LAN), a mobile communicationsnetwork, a satellite communications network, the Internet, a public orprivate cloud, a hybrid cloud, a server farm, any combination thereof,etc. Any localized or distributed architecture may be used withoutdeviating from the scope of the invention.

It should be noted that some of the system features described in thisspecification have been presented as modules, in order to moreparticularly emphasize their implementation independence. For example, amodule may be implemented as a hardware circuit comprising custom verylarge scale integration (VLSI) circuits or gate arrays, off-the-shelfsemiconductors such as logic chips, transistors, or other discretecomponents. A module may also be implemented in programmable hardwaredevices such as field programmable gate arrays, programmable arraylogic, programmable logic devices, graphics processing units, or thelike.

A module may also be at least partially implemented in software forexecution by various types of processors. An identified unit ofexecutable code may, for instance, include one or more physical orlogical blocks of computer instructions that may, for instance, beorganized as an object, procedure, or function. Nevertheless, theexecutables of an identified module need not be physically locatedtogether, but may include disparate instructions stored in differentlocations that, when joined logically together, comprise the module andachieve the stated purpose for the module. Further, modules may bestored on a computer-readable medium, which may be, for instance, a harddisk drive, flash device, RAM, tape, and/or any other suchnon-transitory computer-readable medium used to store data withoutdeviating from the scope of the invention.

Indeed, a module of executable code could be a single instruction, ormany instructions, and may even be distributed over several differentcode segments, among different programs, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within modules, and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set, or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, merely as electronic signals on a system ornetwork.

FIG. 6A illustrates an example of a neural network 600 that has beentrained to recognize graphical elements in an image, according to anembodiment of the present invention. Here, neural network 600 receivespixels of a screenshot image of a 1920×1080 screen as input for input“neurons” 1 to I of the input layer. In this case, I is 2,073,600, whichis the total number of pixels in the screenshot image.

Neural network 600 also includes a number of hidden layers. Both DLNNsand SLNNs usually have multiple layers, although SLNNs may only have oneor two layers in some cases, and normally fewer than DLNNs. Typically,the neural network architecture includes an input layer, multipleintermediate layers, and an output layer, as is the case in neuralnetwork 600.

A DLNN often has many layers (e.g., 10, 50, 200, etc.) and subsequentlayers typically reuse features from previous layers to compute morecomplex, general functions. A SLNN, on the other hand, tends to haveonly a few layers and train relatively quickly since expert features arecreated from raw data samples in advance. However, feature extraction islaborious. DLNNs usually do not require expert features, but tend totake longer to train and have more layers.

For both approaches, the layers are trained simultaneously on thetraining set, normally checking for overfitting on an isolatedcross-validation set. Both techniques can yield excellent results, andthere is considerable enthusiasm for both approaches. The optimal size,shape, and quantity of individual layers varies depending on the problemthat is addressed by the respective neural network.

Returning to FIG. 6A, pixels provided as the input layer are fed asinputs to the J neurons of hidden layer 1. While all pixels are fed toeach neuron in this example, various architectures are possible that maybe used individually or in combination including, but not limited to,feed forward networks, radial basis networks, deep feed forwardnetworks, deep convolutional inverse graphics networks, convolutionalneural networks, recurrent neural networks, artificial neural networks,long/short term memory networks, gated recurrent unit networks,generative adversarial networks, liquid state machines, auto encoders,variational auto encoders, denoising auto encoders, sparse autoencoders, extreme learning machines, echo state networks, Markov chains,Hopfield networks, Boltzmann machines, restricted Boltzmann machines,deep residual networks, Kohonen networks, deep belief networks, deepconvolutional networks, support vector machines, neural Turing machines,or any other suitable type or combination of neural networks withoutdeviating from the scope of the invention.

Hidden layer 2 receives inputs from hidden layer 1, hidden layer 3receives inputs from hidden layer 2, and so on for all hidden layersuntil the last hidden layer provides its outputs as inputs for theoutput layer. It should be noted that numbers of neurons I, J, K, and Lare not necessarily equal, and thus, any desired number of layers may beused for a given layer of neural network 600 without deviating from thescope of the invention. Indeed, in certain embodiments, the types ofneurons in a given layer may not all be the same.

Neural network 600 is trained to assign a confidence score to graphicalelements believed to have been found in the image. In order to reducematches with unacceptably low likelihoods, only those results with aconfidence score that meets or exceeds a confidence threshold may beprovided in some embodiments. For instance, if the confidence thresholdis 80%, outputs with confidence scores exceeding this amount may be usedand the rest may be ignored. In this case, the output layer indicatesthat two text fields, a text label, and a submit button were found.Neural network 600 may provide the locations, dimensions, images, and/orconfidence scores for these elements without deviating from the scope ofthe invention, which can be used subsequently by an RPA robot or anotherprocess that uses this output for a given purpose.

It should be noted that neural networks are probabilistic constructsthat typically have a confidence score. This may be a score learned bythe AI/ML model based on how often a similar input was correctlyidentified during training. For instance, text fields often have arectangular shape and a white background. The neural network may learnto identify graphical elements with these characteristics with a highconfidence. Some common types of confidence scores include a decimalnumber between 0 and 1 (which can be interpreted as a percentage ofconfidence), a number between negative ∞ and positive ∞, or a set ofexpressions (e.g., “low,” “medium,” and “high”). Various post-processingcalibration techniques may also be employed in an attempt to obtain amore accurate confidence score, such as temperature scaling, batchnormalization, weight decay, negative log likelihood (NLL), etc.

“Neurons” in a neural network are mathematical functions that that aretypically based on the functioning of a biological neuron. Neuronsreceive weighted input and have a summation and an activation functionthat governs whether they pass output to the next layer. This activationfunction may be a nonlinear thresholded activity function where nothinghappens if the value is below a threshold, but then the functionlinearly responds above the threshold (i.e., a rectified linear unit(ReLU) nonlinearity). Summation functions and ReLU functions are used indeep learning since real neurons can have approximately similar activityfunctions. Via linear transforms, information can be subtracted, added,etc. In essence, neurons act as gating functions that pass output to thenext layer as governed by their underlying mathematical function. Insome embodiments, different functions may be used for at least someneurons.

An example of a neuron 610 is shown in FIG. 6B. Inputs x₁, x₂, . . . ,x_(n) from a preceding layer are assigned respective weights w₁, w₂, . .. , w_(n). Thus, the collective input from preceding neuron 1 is w₁x₁.These weighted inputs are used for the neuron's summation functionmodified by a bias, such as:

$\begin{matrix}{{\sum\limits_{i = 1}^{m}( {w_{i}x_{i}} )} + {bias}} & (1)\end{matrix}$

This summation is compared against an activation function ƒ(x) todetermine whether the neuron “fires”. For instance, ƒ(x) may be givenby:

$\begin{matrix}{{f(x)} = \{ \begin{matrix}{1\ } & {{{{if}\mspace{14mu}{\sum{wx}}} + {bias}}\  \geq 0} \\{0\ } & {{{{if}\mspace{14mu}{\sum{wx}}} + {bias}}\  < 0}\end{matrix} } & (2)\end{matrix}$

The output y of neuron 710 may thus be given by:

$\begin{matrix}{y = {{{f(x)}{\sum\limits_{i = 1}^{m}( {w_{i}x_{i}} )}} + {bias}}} & (3)\end{matrix}$

In this case, neuron 610 is a single-layer perceptron. However, anysuitable neuron type or combination of neuron types may be used withoutdeviating from the scope of the invention.

A goal, or “reward function,” is often employed. In this example, thegoal is the successful identification of graphical elements in theimage. A reward function explores intermediate transitions and stepswith both short term and long term rewards to guide the search of astate space and attempt to achieve a goal (e.g., successfulidentification of graphical elements, successful identification of anext sequence of activities for an RPA workflow, etc.).

During training, various labeled data (in this case, images) are fedthrough neural network 600. Successful identifications strengthenweights for inputs to neurons, whereas unsuccessful identificationsweaken them. A cost function, such as mean square error (MSE) orgradient descent, may be used to punish predictions that are slightlywrong much less than predictions that are very wrong. If the performanceof the AI/ML model is not improving after a certain number of trainingiterations, a data scientist may modify the reward function, provideindications of where non-identified graphical elements are, providecorrections of misidentified graphical elements, etc.

Backpropagation is a technique for optimizing synaptic weights in afeedforward neural network. Backpropagation may be used to “pop thehood” on the hidden layers of the neural network to see how much of theloss every node is responsible for, and subsequently updating theweights in such a way that minimizes the loss by giving the nodes withhigher error rates lower weights, and vice versa. In other words,backpropagation allows data scientists to repeatedly adjust the weightsso as to minimize the difference between actual output and desiredoutput.

The backpropagation algorithm is mathematically founded in optimizationtheory. In supervised learning, training data with a known output ispassed through the neural network and error is computed with a costfunction from known target output, which gives the error forbackpropagation. Error is computed at the output, and this error istransformed into corrections for network weights that will minimize theerror.

In the case of supervised learning, an example of backpropagation isprovided below. A column vector input x is processed through a series ofN nonlinear activity functions ƒ_(i) between each layer i=1, . . . , Nof the network, with the output at a given layer first multiplied by asynaptic matrix W_(i), and with a bias vector b_(i) added. The networkoutput o, given by

$\begin{matrix}{o = {f_{N}( {{W_{N}{f_{N - 1}( {{W_{N - 1}{f_{N - 2}( \;{.\;.\;.\;{f_{1}( {{W_{1}x} + b_{1}} )}\;.\;.\;.}\; )}} + b_{N - 1}} )}} + b_{N}} )}} & (4)\end{matrix}$

In some embodiments, o is compared with a target output t, resulting inan _(error E=)½∥o−t∥², which is desired to be minimized.

Optimization in the form of a gradient descent procedure may be used tominimize the error by modifying the synaptic weights W_(i) for eachlayer. The gradient descent procedure requires the computation of theoutput o given an input x corresponding to a known target output t, andproducing an error o−t. This global error is then propagated backwardsgiving local errors for weight updates with computations similar to, butnot exactly the same as, those used for forward propagation. Inparticular, the backpropagation step typically requires an activityfunction of the form P_(j)(n_(j))=ƒ_(j)′(n_(j)), where n_(j) is thenetwork activity at layer j (i.e., n_(j)=W_(j)o_(j−1)+b_(j)) whereo_(j)=ƒ_(j)(n_(j)) and the apostrophe ' denotes the derivative of theactivity function ƒ.

The weight updates may be computed via the formulae:

$\begin{matrix}{d_{j} = \{ \begin{matrix}{{( {o - t} ) \circ {p_{j}( n_{j} )}}\ ,} & {j = N} \\{{W_{j + 1}^{T}{d_{j + 1} \circ {p_{j}( n_{j} )}}}\ ,} & {j < N}\end{matrix} } & (5) \\{\frac{\partial E}{\partial W_{j + 1}} = {d_{j + 1}( o_{j} )}^{T}} & (6) \\{\frac{\partial E}{\partial b_{j + 1}} = d_{j + 1}} & (7) \\{W_{j}^{new} = {W_{j}^{old} - {\eta\frac{\partial E}{\partial W_{j}}}}} & (8) \\{b_{j}^{new} = {b_{j}^{old} - {\eta\frac{\partial E}{\partial b_{j}}}}} & (9)\end{matrix}$

where ∘ denotes a Hadamard product (i.e., the element-wise product oftwo vectors), ^(T) denotes the matrix transpose, and o_(j) denotesƒ_(j)(W_(j)o_(j−1)+b_(j)), with o₀=x. Here, the learning rate η ischosen with respect to machine learning considerations. Below, η isrelated to the neural Hebbian learning mechanism used in the neuralimplementation. Note that the synapses W and b can be combined into onelarge synaptic matrix, where it is assumed that the input vector hasappended ones, and extra columns representing the b synapses aresubsumed to W.

The AI/ML model is trained over multiple epochs until it reaches a goodlevel of accuracy (e.g., 97% or better using an F2 or F4 threshold fordetection and approximately 2,000 epochs). This accuracy level may bedetermined in some embodiments using an F1 score, an F2 score, an F4score, or any other suitable technique without deviating from the scopeof the invention. Once trained on the training data, the AI/ML model istested on a set of evaluation data that the AI/ML model has notencountered before. This helps to ensure that the AINIL model is not“over fit” such that it identifies graphical elements in the trainingdata well, but does not generalize well to other images.

In some embodiments, it may not be known what accuracy level may beachieved. Accordingly, if the accuracy of the AI/ML model is starting todrop when analyzing the evaluation data (i.e., the model is performingwell on the training data, but is starting to perform less well on theevaluation data), the AI/ML model may go through more epochs of trainingon the training data (and/or new training data). In some embodiments,the AI/ML model is only deployed if the accuracy reaches a certain levelor if the accuracy of the trained AI/ML model is superior to an existingdeployed AI/ML model.

In certain embodiments, a collection of trained AI/ML models may be usedto accomplish a task, such as employing an AI/ML model for each type ofgraphical element of interest (e.g., one for images, another for textfields, another for radio buttons, etc.), employing an AI/ML model toperform OCR, deploying yet another AI/ML model to recognize proximityrelationships between graphical elements, employing still another AI/MLmodel to generate an RPA workflow based on the outputs from the otherAI/ML models, etc. This may collectively allow the AI/ML models toenable semantic automation, for instance. CV and OCR may be performedusing convolutional and/or recurrent neural networks (RNNs), forexample.

Some embodiments may use transformer networks such asSentenceTransformers™, which is a Python™ framework for state-of-the-artsentence, text, and image embeddings. Such transformer networks learnassociations of words and phrases that have both high scores and lowscores. This trains the AI/ML model to determine what is close to theinput and what is not, respectively. Rather than just using pairs ofwords/phrases, transformer networks may use the field length and fieldtype, as well.

By way of nonlimiting example, consider a workflow in which a user of anRPA designer application includes sequences for opening a web browserand searching for certain information on the Internet where the browsedwebpage contains a table. The user may then add activities to open anExcel® workbook and copy-and-paste this table into an Excel®spreadsheet. In the background, the RPA designer application may trackthe actions taken by the user as the user creates workflows and consultone or more ML models after each activity or a sequence of activities.If the user tends to include this sequence of activities repeatedlyfollowing adding a certain activity, the ML model(s) may learn topredict that the user will likely perform this sequence of actions basedon a certain context and beginning activity (e.g., when the user adds anactivity that launches a web browser, the user then adds activities tovisit the website and copy-and-paste the table into the Excel®spreadsheet).

Once this sequence is learned (e.g., user-specific, common among usersin a company, common globally, etc.), upon adding the task to open theweb browser, the ML model may indicate that one or more next sequencesof activities meet or exceed the suggestion confidence level, and theRPA designer application may prompt the user with the choice to select anext sequence to add to the RPA workflow. Alternatively, in someembodiments, the RPA designer application may automatically add thesequence to the RPA workflow without the user's input when thesuggestion confidence threshold is met or exceeded, or select a nextsequence with the highest confidence threshold when multiple sequencesmeet or exceed the suggestion confidence threshold. In still otherembodiments, the RPA designer application may prompt the user with thechoice to add the learned sequence to the RPA workflow if the confidencelevel of a next sequence of activities is below a relative certaintythreshold but above a suggestion confidence threshold and automaticallyadd the sequence to the workflow without the user's input if theconfidence level of the next sequence of activities is at or above therelative certainty threshold. For example, the sequence of the RPAworkflow may include automatically adding a workbook path inside theExcel Application scope, dropping a “Write Cell” or “Write Range”activity based on the ML model, rename the sheet as per a convention tosuit the current problem, write the results into the Excel® spreadsheet,and drop a “Log Message” activity to write logs regarding the progress.The RPA designer application may complete the workflow automaticallywhen the user clicks an “Enter Key” on the screen, for example.Furthermore, after predicting that the user wishes to write the table inan Excel® spreadsheet, the RPA designer application in this examplegives an appropriate name to the file, starting cell, and sheet name,provides variable declarations, provides property declarations, and logsa message to the user regarding whether the operations were successful.

In general, completion of an RPA workflow accomplishing the task ofopening Excel®, naming a file, entering a starting cell, entering asheet name, and declaring the variables takes around 65-75 seconds foran experienced user to complete. To save time in completing these steps,which tend to be generic to Excel® writing operations, the ML model ofsome embodiments may predict the next sequence of the RPA workflow, asuggestion may be provided to the developer, and the developer mayaccept the suggestion in 2-3 seconds, depending on computing power ofthe developer's computing system. This decreases development time byover one minute in this example. Where an RPA developer creates RPAworkflows with such a sequence frequently, the savings in developmenttime can be substantial.

If the user/developer is satisfied with the predicted next sequence ofactivities, this next sequence of activities may be added to the RPAworkflow. If user/developer is not satisfied with the predicted nextsequence of the RPA workflow provided by the ML model (e.g., the user'spersonal preferences are different, the user's style of building RPAworkflows is different, the business use case requires something else,there is a logical error, etc.), feedback may be given to retrain the MLmodel. If the feedback is user-specific, the ML model may be retrainedfor that user's preferences and a custom ML model may be created. Overtime, the ML model learns what the user is working on and suggests nextsequences of RPA workflows accordingly. If the feedback is notuser-specific (e.g., for a global model or a model for a larger group ofusers than just the individual user), the feedback may be collected withfeedback from other users over a period of time, and the ML model maythen be retrained to be more accurate for all users or the group ofusers.

FIG. 7 is a flowchart illustrating a process 700 for training AI/MLmodel(s) to provide suggestions to automatically add to (i.e.,supplement) and/or complete RPA workflows, according to an embodiment ofthe present invention. The process begins with providing labeled screens(e.g., with graphical elements and text identified), RPA workflows inXAML or any other suitable format for processing, words and phrases, a“thesaurus” of semantic associations between words and phrases such thatsimilar words and phrases for a given word or phrase can be identified,etc. at 710. The AI/ML model is then trained over multiple epochs at 720and results are reviewed at 730.

If the AI/ML model fails to meet a desired confidence threshold at 740,the training data is supplemented and/or the reward function is modifiedto help the AI/ML model more effectively achieve its objectives at 750and the process returns to step 720. If the AI/ML model meets theconfidence threshold at 740, the AI/ML model is tested on evaluationdata at 760 to ensure that the AI/ML model generalizes well and that theAI/ML model is not overly fit with respect to the training data. Theevaluation data may include RPA workflows that the AI/ML model has notprocessed before, for example. If the confidence threshold is met at 770for the evaluation data, the AI/ML model is deployed at 780. If not, theprocess returns to step 750 and the AI/ML model is trained further.

FIG. 8A is a screenshot 800 illustrating an RPA designer application 800after a potential next sequence of activities has been detected by an MLmodel, according to an embodiment of the present invention. Here, thedeveloper has dropped an Excel® Application Scope activity 810 into theRPA workflow. The ML model, which is run by the RPA designer applicationin this embodiment, analyzes the logic of the RPA workflow anddetermines that a subsequent sequence of activities may be desired bythe user. The ML model then provides the suggested sequence to the RPAdesigner application, which displays it to the user with a suggestion tocomplete the RPA workflow automatically (i.e., providing a “Press Enterto Auto-Complete” prompt 820).

After the developer presses enter, the sequence is automatically addedto the RPA workflow, as shown in FIG. 8B. In some embodiments, thesequence may also take into account the developer's personal styleand/or preferences. The RPA workflow may not be complete, or thedeveloper can choose to add additional actions to the RPA workflow iffurther tasks are to be accomplished. Variables and properties are alsoautomatically completed based on the current RPA workflow logic invariables tab 830 (see FIG. 8C) and properties tab 840 (see FIG. 8D),respectively.

FIG. 9 is a flow diagram illustrating a process 900 for rejecting oraccepting and automatically completing a suggested next sequence ofactivities for an RPA workflow, according to an embodiment of thepresent invention. The process begins with a developer creating an RPAworkflow in an RPA designer application, which the RPA designerapplication saves as an XAML workflow as the user adds and modifiesactivities in the RPA workflow. When the user adds or modifies anactivity, the current XAML workflow is sent to an ML model forpreprocessing. During preprocessing, the relevant data from the XAMLfile is extracted, and irrelevant data is stripped. In certainembodiments, the preprocessing may include adding or deriving relevantdata for consideration by the ML model to further improve accuracy(e.g., adding more relevant metadata variables).

After preprocessing, the latest ML model is pulled from a modelinventory database and the latest ML model is loaded and executed on thepreprocessed data. Data resulting from the execution of the ML model isthen passed for inference to the RPA designer application (e.g., XAMLfile(s) including the suggested next sequence(s) of activities), and theRPA designer application uses this data to display the suggestion(s) tothe user/developer. In some embodiments, the confidence score(s) of thesuggestion(s) are also passed to the RPA designer application to makethe determination of whether to suggest the next sequence(s). However,in certain embodiments, if the suggestion confidence threshold is notmet, suggestions may not be passed to the RPA designer application atall. If the user/developer then accepts a suggestion, the RPA developerapplication adds the next sequence of activities to the RPA workflow andthe user/developer may then continue developing the RPA workflow.

If the user rejects the suggestion(s), the user may still continuedeveloping the RPA workflow. However, the XAML of the rejected workflowis then sent to a data inventory database of XAML autocompletesuggestions that were rejected. In certain embodiments, accepted RPAworkflows are also sent as positive examples for retraining. After sometime passes, or when manually instructed to do so, a training module fortraining ML models pulls the rejected autocomplete suggestions (andpotentially accepted positive examples) from the data inventory databaseand uses these to retrain the ML model (e.g., in accordance with process700 of FIG. 7). Once retrained, this latest version of the ML model isthen saved in the model inventory database to be used by the designerapplication.

FIG. 10 is an autocompletion architectural diagram 1000 for both apersonalized and a generalized flow, according to an embodiment of thepresent invention. When a user starts developing the RPA workflow andafter one or more activities are added to the RPA workflow, the initialXAML workflow is passed (1) from the RPA designer application to one ormore retrieved (2) ML models to predict one or more potential nextsequences of activities for suggestion to the user. In some embodiments,the pretrained ML models are personalized (local) and generalized(global). If the local ML model fails to find a sequence for suggestionthat exceeds a suggestion confidence interval, the global ML model maybe used. If no suggestions meet the suggestion confidence threshold, thedesigner application may continue to send XAML workflows as the useradds to and/or modifies the RPA workflow.

If one or more suggestions are provided (e.g., as XAML workflows), theseare suggested (3) to the user in the designer application. Whether theuser accepts or rejects the suggestion(s), and which suggestion wasselected (if any), may be used to update metrics (4) pertaining topredicted activities (e.g., probability scores for given metrics)providing an indication as to how a given ML model is performing. Ifuser rejects the suggested activity or sequence of activities, the usercan continue to build his or her own RPA workflow. The designerapplication then continues to monitor the user's RPA workflow, and aftercompletion thereof, sends the completed RPA workflow (5) to a trainingdatabase as a feedback that will be used as training data in the future.In some embodiments, this data may be used to retrain the local MLmodel, the global ML model, or both.

At some point after storing the user's RPA workflow in the trainingdatabase, the ML model(s) are retrained (6) (e.g., in accordance withprocess 700 of FIG. 7). If the suggestion confidence scores improve overthe previously trained ML model(s), the newly trained ML model(s) willbe considered as the latest best model and will be uploaded (7) to amodel database to serve as the ML model(s) for future processing.

In some embodiments, when the RPA designer application is loaded,multiple ML models may be downloaded and used. For instance, a local MLmodel customized to the user and a global ML model trained using RPAworkflows from multiple or many users may be loaded. The RPA designerapplication may first call the local ML model and see whether it returnsany suggestions (e.g., one or more sequences met or exceeded a 90%suggestion confidence threshold). If so, the suggestion(s) may beprovided to the user. If not, the global ML model may then be called tosee whether one or more suggestions meet or exceed the suggestionconfidence threshold.

Model details for the corresponding ML models (e.g., local and global)may be updated in separate tables in the model database in someembodiments. For instance, the model database may include fields such asmodel ID, model version, model path, model status, and/or any othersuitable fields without deviating from the scope of the invention. Suchfields may be provided when serving the respective ML model.

FIG. 11 is architectural diagram illustrating a system 1100 configuredto train and deploy AI/ML models that provide suggestions toautomatically add to or complete RPA workflows, according to anembodiment of the present invention. System 1100 includes user computingsystems, such as desktop computer 1102, tablet 1104, and smart phone1106. However, any desired computing system may be used withoutdeviating from the scope of invention including, but not limited to,smart watches, laptop computers, etc. Also, while three user computingsystems are shown in FIG. 11, any suitable number of computing systemsmay be used without deviating from the scope of the invention. Forinstance, in some embodiments, dozens, hundreds, thousands, or millionsof computing systems may be used.

Each computing system 1102, 1104, 1106 has a respective RPA robot 1110,1112, 1114 executing an RPA automation that was designed by RPAdevelopers of a development team 1150 using RPA designer applications1154 running on respective RPA developer computing systems 1152. In someembodiments, RPA developer computing systems 1152 may be web-based andmay provide web content to the RPA developer, who is operating anothercomputing system that interacts with RPA developer computing system1152. During development of the RPA workflow for use as an automation byRPA robots 1110, RPA designer applications 1154 call one or more AI/MLmodels 1132 by sending requests that include the RPA workflow indevelopment via a network 1120 (e.g., a local area network (LAN), amobile communications network, a satellite communications network, theInternet, any combination thereof, etc.) to a server 1130 hosting AI/MLmodels 1132. However, in some embodiments, a local ML model running oncalling computing system 1152 is used first, as disclosed above.

In some embodiments, server 1130 may be part of a public cloudarchitecture, a private cloud architecture, a hybrid cloud architecture,etc. In certain embodiments, server 1130 may host multiplesoftware-based servers on a single computing system 1130. In someembodiments, server 1130 may be implemented via one or more virtualmachines (VMs).

Server 1130 provides the received RPA workflow as input to AI/MLmodels(s) 1132 and receives next sequence suggestion(s) and confidencescore(s) therefrom. In some embodiments, server 1130 may only sendsequence(s) that meet a suggestion confidence threshold to the callingRPA designer application 1154. However, in certain embodiments, server1130 passes the suggestion(s) (e.g., RPA workflow(s) or portions thereofto be added) and the associated confidence score(s) to the calling RPAdesigner application 1154.

In some embodiments, a next sequence is accepted automatically if theassociated confidence score meets an autocompletion threshold.Otherwise, the developer may then accept a suggestion if so desired orchoose to reject all suggestions, or potentially reject anautocompletion. The accepted RPA workflows and those where suggestionswere rejected and the developer provided a different implementation aresent to server 1130 and stored in database 1140. This information isthen subsequently used to retrain AI/ML model(s) 1132 (e.g., inaccordance with process 700 of FIG. 7), with the goal of making theretrained version of AI/ML model(s) 1132 more accurate.

Per the above, in some embodiments, multiple AI/ML models 1132 may beused. Each AI/ML model 1132 is an algorithm (or model) that runs on thedata, and AI/ML models 1132 themselves may be a DLNN of trainedartificial “neurons” that are trained in training data, for example.AI/ML models 1132 may be run in series, in parallel, or a combinationthereof.

AI/ML models 1132 may include, but are not limited to, a sequenceextraction model, a clustering detection model, a visual componentdetection model, a text recognition model (e.g., OCR), an audio-to-texttranslation model, or any combination thereof. However, any desirednumber and type(s) of AI/ML models may be used without deviating fromthe scope of the invention. Using multiple AI/ML models 1132 may allowthe system to develop a global picture of what is happening in the RPAworkflows. Patterns may be determined individually by an AI/ML model orcollectively by multiple AI/ML models.

FIG. 12 illustrates an example RPA workflow 1200, according to anembodiment of the present invention. RPA workflow 1200 writes data fromSAP® into an Excel® spreadsheet and provides a log message, ifsuccessful. FIGS. 13A and 13B illustrate a first part 1300 and a secondpart 1310 of the XAML in an RPA designer application that has beenprepared based on activities added and configured by an RPA developer upto a certain point in developing RPA workflow 1200. FIGS. 14A and 14Billustrate a first part 1400 and a second part 1410 of the XAML for RPAworkflow 1200 with XAML 1412 for a predicted next suitable activityhighlighted in grey. The trained AI/ML model would provide prediction1412 based on the other components that are present in RPA workflow 1200up to that point.

An example of a Main.xaml for UiPath Studio™ including files used totrain the AI/ML model in an embodiment is provided below.

<Activity mc:Ignorable=“sap sap2010” x:Class=“Main”mva:VisualBasic.Settings=“{x:Null}”sap:VirtualizedContainerService.HintSize=“824,680.8”sap2010:WorkflowViewState.IdRef=“ActivityBuilder_1”xmlns=“http://schemas.microsoft.com/netfx/2009/xaml/activities”xmlns:mc=“http://schemas.openxmlformats.org/markup-compatibility/2006”xmlns:mva=“clr-namespace:Microsoft.VisualBasic.Activities;assembly=System.Activities”xmlns:sap=“http://schemas.microsoft.com/netfx/2009/xaml/activities/presentation”xmlns:sap2010=“http://schemas.microsoft.com/netfx/2010/xaml/activities/presentation“ xmlns:scg=“clr-namespace:System.Collections.Generic;assembly=mscorlib”xmlns:sd=“clr-namespace:System.Data;assembly=System.Data”xmlns:ui=“http://schemas.uipath.com/workflow/activities”xmlns:x=“http://schemas.microsoft.com/winfx/2006/xaml”><TextExpression.NamespacesForImplementation> <scg:Listx:TypeArguments=“x:String” Capacity=“28”><x:String>System.Activities</x:String><x:String>System.Activities.Statements</x:String><x:String>System.Activities.Expressions</x:String><x:String>System.Activities.Validation</x:String><x:String>System.Activities.XamlIntegration</x:String><x:String>Microsoft.VisualBasic</x:String><x:String>Microsoft.VisualBasic.Activities</x:String><x:String>System</x:String> <x:String>System.Collections</x:String><x:String>System.Collections.Generic</x:String><x:String>System.Data</x:String> <x:String>System.Diagnostics</x:String><x:String>System.Drawing</x:String> <x:String>System.IO</x:String><x:String>System.Linq</x:String> <x:String>System.Net.Mail</x:String><x:String>System.Xml</x:String> <x:String>System.Xml.Linq</x:String><x:String>UiPath.Core</x:String><x:String>UiPath.Core.Activities</x:String><x:String>System.Windows.Markup</x:String><x:String>System.Collections.ObjectModel</x:String><x:String>System.Activities.DynamicUpdate</x:String><x:String>UiPath.Excel</x:String><x:String>UiPath.Excel.Activities</x:String><x:String>System.ComponentModel</x:String><x:String>System.Runtime.Serialization</x:String><x:String>System.Xml.Serialization</x:String> </scg:List></TextExpression.NamespacesForImplementation><TextExpression.ReferencesForImplementation> <scg:Listx:TypeArguments=“AssemblyReference” Capacity=“21”><AssemblyReference>System.Activities</AssemblyReference><AssemblyReference>Microsoft.VisualBasic</AssemblyReference><AssemblyReference>mscorlib</AssemblyReference><AssemblyReference>System.Data</AssemblyReference><AssemblyReference>System</AssemblyReference><AssemblyReference>System.Drawing</AssemblyReference><AssemblyReference>System.Core</AssemblyReference><AssemblyReference>System.Xml</AssemblyReference><AssemblyReference>System.Xml.Linq</AssemblyReference><AssemblyReference>PresentationFramework</AssemblyReference><AssemblyReference>WindowsBase</AssemblyReference><AssemblyReference>PresentationCore</AssemblyReference><AssemblyReference>System.Xaml</AssemblyReference><AssemblyReference>UiPath.System.Activities</AssemblyReference><AssemblyReference>UiPath.UiAutomation.Activities</AssemblyReference><AssemblyReference>System.Data.DataSetExtensions</AssemblyReference><AssemblyReference>UiPath.Excel.Activities.Design</AssemblyReference><AssemblyReference>UiPath.Excel.Activities</AssemblyReference><AssemblyReference>UiPath.Excel</AssemblyReference><AssemblyReference>System.Runtime.Serialization</AssemblyReference><AssemblyReference>UiPath.System.Activities.Design</AssemblyReference></scg:List> </TextExpression.ReferencesForImplementation> <Sequencesap:VirtualizedContainerService.HintSize=“475.2,616”sap2010:WorkflowViewState.IdRef=“Sequence_ 1”> <Sequence.Variables><Variable x:TypeArguments=“sd:DataTable” Name=“results” /> <Variablex:TypeArguments=“sd:DataTable” Name=“form_details” /></Sequence.Variables> <sap:WorkflowViewStateService.ViewState><scg:Dictionary x:TypeArguments=“x:String, x:Object”> <x:Booleanx:Key=“IsExpanded”>True</x:Boolean> </scg:Dictionary></sap:WorkflowViewStateService.ViewState> <Assignsap:VirtualizedContainerService.HintSize=“433.6,60”sap2010:WorkflowViewState.IdRef=“Assign_1”> <Assign.To> <OutArgumentx:TypeArguments=“sd:DataTable”>[results]</OutArgument> </Assign.To><Assign.Value> <InArgumentx:TypeArguments=“sd:DataTable”>[form_details]</InArgument></Assign.Value> </Assign> <ui:ExcelApplicationScope Password=“{x:Null}”DisplayName=“Excel Application Scope”sap:VirtualizedContainerService.HintSize=“433.6,290.4”sap2010:WorkflowViewState.IdRef=“ExcelApplicationScope_1”InstanceCachePeriod=“3000” WorkbookPath=“Form Details.xlsx”><ui:ExcelApplicationScope.Body> <ActivityActionx:TypeArguments=“ui:WorkbookApplication”> <ActivityAction.Argument><DelegateInArgument x:TypeArguments=“ui:WorkbookApplication”Name=“ExcelWorkbookScope” /> </ActivityAction.Argument> <SequenceDisplayName=“Do” sap:VirtualizedContainerService.HintSize=“375.2,180.8”sap2010:WorkflowViewState.IdRef=“Sequence_2”><sap:WorkflowViewStateService.ViewState> <scg:Dictionaryx:TypeArguments=“x:String, x:Object”> <x:Booleanx:Key=“IsExpanded”>True</x:Boolean> </scg:Dictionary></sap:WorkflowViewStateService.ViewState> <ui:ExcelWriteRangeAddHeaders=“False” DataTable=“[results]” DisplayName=“Write Range”sap:VirtualizedContainerService.HintSize=“333.6,88”sap2010:WorkflowViewState.IdRef=“ExcelWriteRange_1” SheetName=“Sheet1”StartingCell=“A1” /> </Sequence> </ActivityAction></ui:ExcelApplicationScope.Body> </ui:ExcelApplicationScope><ui:LogMessage DisplayName=“Log Message”sap:VirtualizedContainerService.HintSize=“433.6,92.8”sap2010:WorkflowViewState.IdRef=“LogMessage_1” Level=“Info”Message=“[&quot;Excel data successfully written&quot;]” /> </Sequence></Activity>

Such XAML files may be provided for various RPA workflows beingdeveloped by RPA developers, per the above. The AI/ML model may usethese examples to be trained to recognize patterns and provide nextsequence suggestions. Once in production use, more data may be collectedas described above, and the AI/ML model may be retrained to improve itsaccuracy, local AI/ML models may be generated to accommodate individualuser preferences, etc.

FIG. 15 is a flowchart illustrating a process 1500 for automaticallycompleting RPA workflows using ML, according to an embodiment of thepresent invention. The process begins capturing created RPA workflows,sequences of activities in the created RPA workflows, or both, by an RPAdesigner application and storing them in a database at 1505. In someembodiments, the RPA workflows, sequences of activities, or both, may bein XAML format, JSON format, etc. The stored RPA workflows, sequences ofactivities, or both, are then used to train one or more ML models at1510. The trained ML model(s) are then deployed to user computingsystems or otherwise made available to users at 1515.

Once deployed or made available, the RPA designer application monitorsuser activities during RPA workflow development and provides these to atleast one of the one or more ML models at 1520 (e.g., as XAML, JSON,etc.). In some embodiments, multiple ML models may be called andexecuted in series if a previously executed ML model does not detect anext sequence of activities. If the ML model(s) do not detect one ormore potential next sequences of activities meeting or exceeding asuggestion confidence threshold at 1525 (e.g., as determined on theserver side or by the RPA designer application itself based on theconfidence score(s) for the suggestion(s)), the process returns to step1520. However, if one or more potential next sequences of activitiesmeeting or exceeding the suggestion confidence threshold are detected at1525, the sequence(s) are suggested to the user at 1530.

If the user accepts the suggestion or if the sequence exceeds a second,higher autocompletion threshold that does not require user selection at1535, the suggested sequence of activities is automatically added to theRPA workflow at 1540. However, if the user rejects the suggestion at1535, the RPA designer application waits for the user to complete theRPA workflow and then causes the completed RPA workflow to be stored at1545 (e.g., by sending the completed RPA workflow to a cloud RPAsystem). The completed RPA workflow and potentially some or many othercompleted RPA workflows including negative examples (and potentiallypositive examples) are then used to retrain the ML model(s) at 1550, andthe retrained ML model(s) are deployed or made available at 1555.

The process steps performed in FIG. 15 may be performed by a computerprogram, encoding instructions for the processor(s) to perform at leastpart of the process(es) described in FIG. 15, in accordance withembodiments of the present invention. The computer program may beembodied on a non-transitory computer-readable medium. Thecomputer-readable medium may be, but is not limited to, a hard diskdrive, a flash device, RAM, a tape, and/or any other such medium orcombination of media used to store data. The computer program mayinclude encoded instructions for controlling processor(s) of a computingsystem (e.g., processor(s) 510 of computing system 500 of FIG. 5) toimplement all or part of the process steps described in FIG. 15, whichmay also be stored on the computer-readable medium.

The computer program can be implemented in hardware, software, or ahybrid implementation. The computer program can be composed of modulesthat are in operative communication with one another, and which aredesigned to pass information or instructions to display. The computerprogram can be configured to operate on a general purpose computer, anASIC, or any other suitable device.

It will be readily understood that the components of various embodimentsof the present invention, as generally described and illustrated in thefigures herein, may be arranged and designed in a wide variety ofdifferent configurations. Thus, the detailed description of theembodiments of the present invention, as represented in the attachedfigures, is not intended to limit the scope of the invention as claimed,but is merely representative of selected embodiments of the invention.

The features, structures, or characteristics of the invention describedthroughout this specification may be combined in any suitable manner inone or more embodiments. For example, reference throughout thisspecification to “certain embodiments,” “some embodiments,” or similarlanguage means that a particular feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment of the present invention. Thus, appearances of the phrases“in certain embodiments,” “in some embodiment,” “in other embodiments,”or similar language throughout this specification do not necessarily allrefer to the same group of embodiments and the described features,structures, or characteristics may be combined in any suitable manner inone or more embodiments.

It should be noted that reference throughout this specification tofeatures, advantages, or similar language does not imply that all of thefeatures and advantages that may be realized with the present inventionshould be or are in any single embodiment of the invention. Rather,language referring to the features and advantages is understood to meanthat a specific feature, advantage, or characteristic described inconnection with an embodiment is included in at least one embodiment ofthe present invention. Thus, discussion of the features and advantages,and similar language, throughout this specification may, but do notnecessarily, refer to the same embodiment.

Furthermore, the described features, advantages, and characteristics ofthe invention may be combined in any suitable manner in one or moreembodiments. One skilled in the relevant art will recognize that theinvention can be practiced without one or more of the specific featuresor advantages of a particular embodiment. In other instances, additionalfeatures and advantages may be recognized in certain embodiments thatmay not be present in all embodiments of the invention.

One having ordinary skill in the art will readily understand that theinvention as discussed above may be practiced with steps in a differentorder, and/or with hardware elements in configurations which aredifferent than those which are disclosed. Therefore, although theinvention has been described based upon these preferred embodiments, itwould be apparent to those of skill in the art that certainmodifications, variations, and alternative constructions would beapparent, while remaining within the spirit and scope of the invention.In order to determine the metes and bounds of the invention, therefore,reference should be made to the appended claims.

1. A system, comprising: a developer computing system executing arobotic process automation (RPA) designer application; and a modelserving server hosting one or more artificial intelligence (AI) /machine learning (ML) models trained to analyze sequences of activitiesin an RPA workflow as input and provide suggestions of next sequences ofactivities and respective confidence scores as an output, wherein theRPA designer application is configured to: capture a sequence of theactivities in an RPA workflow, send the captured sequence of activitiesto the model serving server, receive one or more suggested nextsequences of activities from the one or more trained AI/ML models viathe model serving server, and display the one or more suggested nextsequences of activities to the developer.
 2. The system of claim 1,wherein the model serving server is configured to: receive the capturedsequence of activities from the RPA designer application of thedeveloper computing system; provide the captured sequence of activitiesto the one or more AI/ML models as an input; execute the one or moreAI/ML models; and receive the one or more suggested next sequences ofactivities and the respective confidence scores as an output from theone or more AI/ML models.
 3. The system of claim 1, wherein the modelserving server is configured to send sequences of the one or moresuggested next sequences of activities to the RPA designer applicationthat meet or exceed a suggestion confidence threshold.
 4. The system ofclaim 1, further comprising: a database storing RPA workflows from RPAdesigner applications, the RPA workflows comprising captured sequencesof activities, wherein the model serving server or a training server isconfigured to: train the one or more AI/ML models using the storedcaptured sequences of activities and the stored RPA workflows in thedatabase to identify one or more next sequences of activities in RPAworkflows being developed in RPA designer applications.
 5. The system ofclaim 4, wherein the model serving server or the retraining server isconfigured to retrain the one or more trained AI/ML models after apredetermined period of time has passed, after a predetermined amount ofdata has been collected since a last training of the one or more trainedAI/ML models, after a predetermined number of developers haveautomatically completed RPA workflows, after a predetermined number orpercentage of developers have rejected suggestions from the one or moretrained AI/ML models, or any combination thereof.
 6. The system of claim4, wherein during training, the one or more trained AI/ML models learndeveloper-specific style, logic, conventions, or any combinationthereof, as a developer develops RPA workflows over time.
 7. The systemof claim 1, wherein the one or more trained AI/ML models are configuredto: detect that one or more of the added and/or modified activitieswithin the RPA designer application are indicative of a next sequence ofactivities based on the RPA workflow as input as the developer addsand/or modifies the activities in the RPA workflow, the detection basedon running parameters of the RPA workflow through the one or moretrained AI/ML models and producing a sequence of next steps and asuggestion confidence threshold as an output.
 8. The system of claim 1,wherein after the RPA designer application displays the one or moresuggested next sequences of activities, when the developer providesconfirmation in the RPA designer application that a sequence of the oneor more suggested next sequences of activities is correct, the RPAdesigner application is configured to automatically add the nextsequence of activities to the RPA workflow.
 9. The system of claim 8,wherein the automatically adding of the next sequence of activities tothe workflow comprises setting declarations and usage of variables,setting properties, reading from and/or writing to files, or anycombination thereof.
 10. The system of claim 1, wherein when one or moresequences of the suggested next sequences of activities meets or exceedsan automatic completion confidence threshold that is higher than thesuggestion confidence threshold, the RPA designer application isconfigured to automatically add a sequence of the one or more suggestednext sequences of activities that meets or exceeds the automaticcompletion confidence threshold with a highest confidence score.
 11. Thesystem of claim 1, wherein after the one or more trained AI/ML modelssuggests the next sequence of activities, when a developer provides anindication in the RPA designer application that the one or more nextsequences of activities are incorrect, the RPA designer application isconfigured to cause the RPA workflow to be stored in the database as anegative example for subsequent retraining of the one or more trainedAI/ML models after the developer completes the RPA workflow.
 12. Thesystem of claim 1, wherein the suggestion confidence threshold is aprobabilistic threshold based on confidence scores learned during thetraining of the one or more trained AI/ML models.
 13. The system ofclaim 1, wherein the one or more trained AI/ML models comprise a localAI/ML model and a global AI/ML model, the RPA designer application isconfigured to call the local AI/ML model first, when the local AI/MLmodel suggests one or more next sequences of activities that meet orexceed the suggestion confidence threshold, the RPA designer applicationis configured to display the one or more next sequences of activitiesfrom the local AI/ML model to the developer, and when the local AI/MLmodel does not suggest at least one next sequences of activities thatmeet or exceed the suggestion confidence threshold, the RPA designerapplication is configured to call the global AI/ML model via the modelserving server.
 14. The system of claim 13, wherein the local AI/MLmodel and the global AI/ML model utilize different suggestion confidencethresholds.
 15. The system of claim 1, wherein when a first AI/ML modelof the one or more trained AI/ML models does not provide a suggestion ofat least one next sequence of activities meeting or exceeding thesuggestion confidence threshold, the RPA designer application isconfigured to call a second AI/ML model of the one or more trained AI/MLmodels, a third AI/ML model of the one or more trained AI/ML models, andso on until at least one next sequence of activities meeting orexceeding the suggestion confidence threshold has been found or all ofthe one or more trained AI/ML models have been called withoutidentifying at least one next sequence of activities meeting orexceeding the suggestion confidence threshold.
 16. The system of claim1, wherein the one or more trained AI/ML models are trained usingattended developer feedback, unattended developer feedback, or both. 17.A non-transitory computer-readable medium storing a computer programcomprising a robotic process automation (RPA) designer application, thecomputer program configured to cause at least one processor to: capturea sequence of the activities in an RPA workflow, the captured sequenceof activities comprising one or more activities that have been added toand/or modified in the RPA workflow by a developer; send the capturedsequence of activities to a model serving server; receive one or moresuggested next sequences of activities from one or more trained AI/MLmodels via the model serving server; and display the one or moresuggested next sequences of activities to the developer.
 18. Thenon-transitory computer-readable medium of claim 17, wherein the one ormore trained AI/ML models are configured to: detect that one or more ofthe added and/or modified activities within the RPA designer applicationare indicative of a next sequence of activities based on the RPAworkflow as input as the developer adds and/or modifies the activitiesin the RPA workflow, the detection based on running parameters of theRPA workflow through the one or more trained AI/ML models and producinga sequence of next steps and a suggestion confidence threshold as anoutput.
 19. The non-transitory computer-readable medium of claim 17,wherein after the computer program displays the one or more suggestednext sequences of activities, when the developer provides confirmationin the RPA designer application that a sequence of the one or moresuggested next sequences of activities is correct, the computer programis configured to automatically add the next sequence of activities tothe RPA workflow.
 20. The non-transitory computer-readable medium ofclaim 19, wherein the automatically adding of the next sequence ofactivities to the workflow comprises setting declarations and usage ofvariables, setting properties, reading from and/or writing to files, orany combination thereof.
 21. The non-transitory computer-readable mediumof claim 17, wherein when one or more sequences of the suggested nextsequences of activities meets or exceeds an automatic completionconfidence threshold that is higher than the suggestion confidencethreshold, the computer program is configured to automatically add asequence of the one or more suggested next sequences of activities thatmeets or exceeds the automatic completion confidence threshold with ahighest confidence score.
 22. The non-transitory computer-readablemedium of claim 17, wherein after the one or more trained AI/ML modelssuggests the next sequence of activities, when a developer provides anindication in the RPA designer application that the one or more nextsequences of activities are incorrect, the computer program isconfigured to cause the RPA workflow to be stored in the database as anegative example for subsequent retraining of the one or more trainedAI/ML models after the developer completes the RPA workflow.
 23. Thenon-transitory computer-readable medium of claim 17, wherein thesuggestion confidence threshold is a probabilistic threshold based onconfidence scores learned during the training of the one or more trainedAI/ML models.
 24. The non-transitory computer-readable medium of claim17, wherein the one or more trained AI/ML models comprise a local AI/MLmodel and a global AI/ML model, the computer program is configured tocall the local AI/ML model first, when the local AI/ML model suggestsone or more next sequences of activities that meet or exceed thesuggestion confidence threshold, the computer program is configured todisplay the one or more next sequences of activities from the localAI/ML model to the developer, and when the local AI/ML model does notsuggest at least one next sequences of activities that meet or exceedthe suggestion confidence threshold, the computer program is configuredto call the global AI/ML model via the model serving server.
 25. Thenon-transitory computer-readable medium of claim 24, wherein the localAI/ML model and the global AI/ML model utilize different suggestionconfidence thresholds.
 26. The non-transitory computer-readable mediumof claim 17, wherein when a first AI/ML model of the one or more trainedAI/ML models does not provide a suggestion of at least one next sequenceof activities meeting or exceeding the suggestion confidence threshold,the computer program is configured to call a second AI/ML model of theone or more trained AI/ML models, a third AI/ML model of the one or moretrained AI/ML models, and so on until at least one next sequence ofactivities meeting or exceeding the suggestion confidence threshold hasbeen found or all of the one or more trained AI/ML models have beencalled without identifying at least one next sequence of activitiesmeeting or exceeding the suggestion confidence threshold.
 27. A modelserving computing system, comprising: memory storing computer programinstructions; and at least one processor configured to execute thecomputer program instructions, wherein the computer program instructionsare configured to cause the at least one processor to: receive acaptured sequence of activities in a robotic process automation (RPA)workflow under development from an RPA designer application of adeveloper computing system via a communication network, provide thecaptured sequence of activities as input to one or more trainedartificial intelligence (AI)/machine learning (ML) models, receive oneor more suggested next sequences of activities and respective confidencescores as an output from the one or more trained AI/ML models, and sendthe one or more suggested next sequences of activities to the designercomputing system.
 28. The model serving computing system of claim 27,wherein the computer program instructions are further configured tocause the at least one processor to: host the one or more trained AI/MLmodels; and execute the one or more trained AI/ML models responsive toreceiving the captured sequence of activities in the RPA workflow. 29.The model serving computing system of claim 27, wherein the computerprogram instructions are further configured to cause the at least oneprocessor to send sequences of the one or more suggested next sequencesof activities to the RPA designer application that meet or exceed asuggestion confidence threshold.
 30. The model serving computing systemof claim 27, wherein the computer program instructions are furtherconfigured to cause the at least one processor to: train the one or moreAI/ML models using stored captured sequences of activities and storedRPA workflows in a database to identify one or more next sequences ofactivities in RPA workflows being developed in RPA designerapplications.
 31. The model serving computing system of claim 30,wherein the computer program instructions are further configured tocause the at least one processor to: retrain the one or more trainedAI/ML models after a predetermined period of time has passed, after apredetermined amount of data has been collected since a last training ofthe one or more trained AI/ML models, after a predetermined number ofdevelopers have automatically completed RPA workflows, after apredetermined number or percentage of developers have rejectedsuggestions from the one or more trained AI/ML models, or anycombination thereof.
 32. The model serving computing system of claim 30,wherein during training, the one or more trained AI/ML models learndeveloper-specific style, logic, conventions, or any combinationthereof, as a developer develops RPA workflows over time.
 33. The modelserving computing system of claim 27, wherein the one or more trainedAI/ML models are configured to: detect that one or more of the addedand/or modified activities within the RPA designer application areindicative of a next sequence of activities based on the RPA workflow asinput as a developer adds and/or modifies the activities in the RPAworkflow, the detection based on running parameters of the RPA workflowthrough the one or more trained AI/ML models and producing a sequence ofnext steps and a suggestion confidence threshold as an output.
 34. Themodel serving computing system of claim 27, wherein when a first AI/MLmodel of the one or more trained AI/ML models does not provide asuggestion of at least one next sequence of activities meeting orexceeding the suggestion confidence threshold, the computer program isconfigured to cause the at least one processor to call a second AI/MLmodel of the one or more trained AI/ML models, a third AI/ML model ofthe one or more trained AI/ML models, and so on until at least one nextsequence of activities meeting or exceeding the suggestion confidencethreshold has been found or all of the one or more trained AI/ML modelshave been called without identifying at least one next sequence ofactivities meeting or exceeding the suggestion confidence threshold.